肺腺癌诊断标志物筛选及免疫细胞浸润分析

用生物信息学方法筛选肺腺癌(Lung adenocarcinoma,LUAD)的诊断生物标志物,并分析肺腺癌中免疫细胞浸润情况。从GEO和TCGA数据库下载肺腺癌的表达数据集,利用R软件筛选肺腺癌与正常肺组织间的差异表达基因(DEGs),使用DAVID网站对DEGs进行GO及KEGG富集分析,使用STRING及Cytoscape等工具对DEGs构建蛋白相互作用网络并筛选hub基因;利用Kaplan-Meier法对DEGs进行生存分析,并对hub基因进行ROC分析筛选诊断生物标志物,利用GSEA预测有预后价值的基因参与的信号通路;并用Cibersort软件反卷积算法分析肺腺癌中免疫细胞浸润情况。共得到肺腺癌的234个DEGs,这些基因主要参与信号转导、物质代谢、免疫反应等相关信号通路;构建PPI网络筛选出的20个hub基因中8个存在预后价值(CCNA2、DLGAP5、HMMR、MMP1、MMP9、MMP13、SPP1、TOP2A),ROC分析中DLGAP5、SPP1值分别是0.703、0.706;DLGAP5、SPP1基因表达水平与肺腺癌组织浆细胞、未活化的CD4⁺记忆细胞、调节T细胞、巨噬细胞M0、M1、M2及中性粒细胞浸润密切相关(P＜0.05)。肺腺癌中DLGAP5、SPP1具有较高诊断价值且参与肺腺癌组织免疫细胞浸润;DLGAP5、SPP1基因可作为肺腺癌诊断的生物标志物,可为肺腺癌的靶向治疗研究提供新思路。     

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein-protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier plot demonstrated that low-immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. -, P < .001). A total of 540 genes were screened as DEGs with 539 up-regulated genes and 1 down-regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine-cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4, MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment-related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.     

三峡库区消落带野生狗牙根对水淹适生性的转录组分析

为探究狗牙根对水淹环境的适生性机制,取长势一致的狗牙根分为未水淹对照组(A1)和30 cm沉水处理组(A2),A2水淹15 d后两组同时采样,提取样本总RNA作转录组测序。A1和A2测序数据经de novo组装分别获得128031和83363条高质量Unigene;A1和A2对比发现28844条差异表达基因(differentially expressed genes, DEGs),其中11858条DEGs上调表达,16986条DEGs下调表达;功能注释显示,这些DEGs主要集中在转录、翻译、碳水化合物代谢和环境适应等途径;信号通路富集显示,包括植物对病原体的防御、物质代谢、光合作用、形态发育和抗氧化等过程的103个信号通路与狗牙根对水淹生境的适生性有关;随机挑选4个DEGs作qRT-PCR分析,荧光定量结果与测序数据基本一致,说明基于转录组分析DEGs的表达情况较为可靠。该工作或可为进一步挖掘与狗牙根水淹适生性相关的调控基因和功能基因提供基础。     

Neurexophilin and PC-esterase domain family member 4 (NXPE4) and prostate androgen-regulated mucin-like protein 1 (PARM1) as prognostic biomarkers for colorectal cancer

Owing to the high morbidity and mortality, novel biomarkers in the occurrence and development of colorectal cancer (CRC) are needed nowadays. In this study, the CRC-related datasets were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. After screening the differentially expressed genes (DEGs) in R software, a total of 238 upregulated and 199 downregulated DEGs were revealed simultaneously. Then the Kaplan-Meier survival analysis and Cox regression analysis were used to reveal the prognostic function of these DEGs. Neurexophilin and PC-esterase domain family member 4 (NXPE4) and prostate androgen-regulated mucin-like protein 1 (PARM1) were two outstanding independent overall survival (OS) and relapse-free survival (RFS) prognostic genes of CRC in TCGA database. We next verified the expression of NXPE4 and PARM1 messenger RNA (mRNA) levels were significantly lower in CRC tumor tissue than in the adjacent noncancerous tissue in our clinical samples, and NXPE4 mRNA expression level was related to the tumor location and tumor size, while PARM1 was related to tumor location, lymph nodes metastasis, and tumor size. This study demonstrated that NXPE4 and PARM1 might be two potential novel prognostic biomarkers for CRC.     

2型猪链球菌脑膜炎小鼠模型的构建及其转录组学分析

【背景】2型猪链球菌(Streptococcus suis serotype 2, S. suis 2)可感染宿主引起严重的脑膜炎,对养猪业和人类公共卫生安全构成重大威胁。【目的】构建S. suis 2感染小鼠脑膜炎模型,并对其脑组织进行转录组学分析,为揭示S.suis2感染宿主后引起脑膜炎的分子机制和发现潜在的治疗靶点提供理论依据。【方法】采用S. suis 2感染小鼠,并对其脑组织进行病理组织学分析确认构建脑膜炎小鼠后,对其脑组织进行转录组学分析,对比S.suis2感染和未感染小鼠的差异表达基因,并对差异表达基因进行基因本体论(geneontology,GO)功能、京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)通路富集和韦恩分析。【结果】脑病理组织学分析结果显示,S. suis 2感染的小鼠脑膜中有大量的炎症细胞浸润,并且血管周围出现“袖套”现象,并能从感染小鼠的组织器官中再分离出攻毒的S. suis 2菌株,结果证明构建了S. suis 2感染脑膜炎小鼠模型。转录组学分析结果表明,感染S.suis2与未感染的...     

Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis

Multiple myeloma (MM) is a common hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. This study aimed to elucidate key candidate genes and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE6477 and GSE47552 were obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) with p < .05 and [logFC] > 1 were identified. Functional enrichment, protein–protein interaction network construction and survival analyses were then performed. First, 51 upregulated and 78 downregulated DEGs shared between the two GSE datasets were identified. Second, functional enrichment analysis showed that these DEGs are mainly involved in the B cell receptor signaling pathway, hematopoietic cell lineage, and NF-kappa B pathway. Moreover, interrelation analysis of immune system processes showed enrichment of the downregulated DEGs mainly in B cell differentiation, positive regulation of monocyte chemotaxis and positive regulation of T cell proliferation. Finally, the correlation between DEG expression and survival in MM was evaluated using the PrognoScan database. In conclusion, we identified key candidate genes that affect the outcomes of patients with MM, and these genes might serve as potential therapeutic targets.     

Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

Comprehensive gene expression analysis reveals multiple signal pathways associated with prostate cancer

Prostate cancer (PC) depends on androgenic signaling for growth and survival. To data, the exact molecular mechanism of hormone controlling proliferation and tumorigenesis in the PC remains unclear. Therefore, in this study, we explored the differentially expressed genes (DEGs) and identified featured genes related to hormone stimulus from PC. Two sets of gene expression data, including PC and normal control sample, were downloaded from Gene Expression Omnibus (GEO) database. The t-test was used to identify DEGs between PC and controls. Gene ontology (GO) functional annotation was applied to analyze the function of DEGs and screen hormone-related DEGs. Then these hormone-related DEGs were further analyzed in constructed cancer network and Human Protein Reference Database to screen important signaling pathways they participated in. A total of 912 DEGs were obtained which included 326 up-regulated genes and 586 down-regulated genes. GO functional enrichment analysis identified 50 hormone-related DEGs associated with PC. After pathway and PPI network analysis, we found these hormone-related DEGs participated in several important signaling pathways including TGF-β (TGFB2, TGFB3 and TGFBR2), MAPK (TGFB2, TGFB3 and TGFBR2), insulin (PIK3R3, SHC1 and EIF4EBP1), and p53 signaling pathways (CCND2 and CDKN1A). In addition, a total of five hormone-related DEGs (SHC1, CAV1, RXRA, CDKN1A and SRF) were located in the center of PPI network and 12 hormone-related DEGs formed six protein modules. These important signal pathways and hormone-related DEGs may provide potential therapeutic targets for PC.     

Identification of a five-gene signature with prognostic value in colorectal cancer

Colorectal cancer (CRC) ranks as one of the most commonly diagnosed malignancies worldwide. Although mortality rates have been decreasing, the prognosis of CRC patients is still highly dependent on the individual. Therefore, identifying and understanding novel biomarkers for CRC prognosis remains crucial. The gene expression profiles of five-gene expression omnibus (GEO) data sets of CRC were first downloaded. A total of 352 consistent differentially expressed genes (DEGs) were identified for CRC and paired with normal tissues. Functional analysis including gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment revealed that these DEGs were related to metabolic pathways, tight junctions, and the cell cycle. Ten hub DEGs were identified based on the search tool for the retrieval of interacting genes database and protein–protein interaction networks. By using univariate Cox proportional hazard regression analysis, we found 11 survival-related genes among these DEGs. We finally established a five-gene signature (kinesin family member 15, N-acetyltransferase 2, glutathione peroxidase 3, secretogranin II, and chloride channel accessory 1) with prognostic value in CRC by step multivariate Cox regression analysis. Based on this risk scoring system, patients in the high-risk group had significantly poorer survival results compared with those in the low-risk group (log-rank test, p < 0.0001). Finally, we validated our gene signature scoring system in two independent GEO cohorts (GSE17536 and GSE33113). We found all five of the signature genes to be DEGs in The Cancer Genome Atlas database. In conclusion, our findings suggest that our five DEG-based signature can provide a novel biomarker with useful applications in CRC prognosis.     

Comprehensive analysis of gene expression profiles to identify differential prognostic factors of primary and metastatic breast cancer

Breast cancer accounts for nearly half of all cancer-related deaths in women worldwide. However, the molecular mechanisms that lead to tumour development and progression remain poorly understood and there is a need to identify candidate genes associated with primary and metastatic breast cancer progression and prognosis. In this study, candidate genes associated with prognosis of primary and metastatic breast cancer were explored through a novel bioinformatics approach. Primary and metastatic breast cancer tissues and adjacent normal breast tissues were evaluated to identify biomarkers characteristic of primary and metastatic breast cancer. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) dataset (ID: HS-01619) was downloaded using the mRNASeq platform. Genevestigator 8.3.2 was used to analyse TCGA-BRCA gene expression profiles between the sample groups and identify the differentially-expressed genes (DEGs) in each group. For each group, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to determine the function of DEGs. Networks of protein–protein interactions were constructed to identify the top hub genes with the highest degree of interaction. Additionally, the top hub genes were validated based on overall survival and immunohistochemistry using The Human Protein Atlas. Of the top 20 hub genes identified, four (KRT14, KIT, RAD51, and TTK) were considered as prognostic risk factors based on overall survival. KRT14 and KIT expression levels were upregulated while those of RAD51 and TTK were downregulated in patients with breast cancer. The four proposed candidate hub genes might aid in further understanding the molecular changes that distinguish primary breast tumours from metastatic tumours as well as help in developing novel therapeutics. Furthermore, they may serve as effective prognostic risk markers based on the strong correlation between their expression and patient overall survival.     

The AMP-dependent kinase pathway is upregulated in BAP1 mutant uveal melanoma

Metastatic uveal melanoma (UM) responds poorly to targeted therapies and immune checkpoint inhibitors. Loss of BRCA1-associated protein 1 (BAP1) via inactivating mutations in the BAP1 gene is associated with UM progression. Thus, molecular alterations caused by BAP1 dysfunction may be novel therapeutic targets for metastatic UM. Here, we found that phosphorylation of AMP-dependent kinase (AMPK) was elevated in BAP1-altered (or mutant) compared to BAP1-unaltered (or wild-type [WT]) UM tumors. As a readout of AMPK pathway activation, phosphorylation of an AMPK downstream effector, acetyl-CoA-carboxylase (ACC), was also elevated. BAP1 re-expression in BAP1-null UM cell lines decreased phospho-AMPK (pAMPK) and phospho-ACC (pACC) levels. AMPK phosphorylation is mediated by calcium/calmodulin dependent protein kinase kinase 2 (CaMKK2) and potentially liver kinase B1 (LKB1) in BAP1 mutant UM cells. Knockdown of AMPKα1/2 reduced the viability of BAP1 mutant UM cells, indicating a survival function of AMPK in BAP1 mutant UM. Our data suggest that the AMPK pathway is an important mechanism mediating the survival of BAP1 mutant UM. Targeting the AMPK pathway may be a novel therapeutic strategy for metastatic UM.     

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.     

The identification of new biomarkers for bladder cancer: A study based on TCGA and GEO datasets

Bladder cancer (BC) is one of the most common neoplastic diseases worldwide. With the highest recurrence rate among all cancers, treatment of BC only improved a little in the last 30 years. Available biomarkers are not sensitive enough for the diagnosis of BC, whereas the standard diagnostic method, cystoscopy, is an invasive test and expensive. Hence, seeking new biomarkers of BC is urgent and challenging. With that order, we screened the overlapped differentially expressed genes (DEGs) of GSE13507 and TCGA BLCA datasets. Subsequent protein–protein interactions network analysis recognized the hub genes among these DEGs. Further functional analysis including Gene Ontology and KEGG pathway analysis and gene set enrichment analysis were processed to investigate the role of these genes and potential underlying mechanisms in BC. Kaplan–Meier analysis and Cox hazard ratio analysis were carried out to clarify the diagnostic and prognostic role of these genes. In conclusion, our present study demonstrated that ACTA2, CDC20, MYH11, TGFB3, TPM1, VIM, and DCN are all potential diagnostic biomarkers for BC. And may also be potential treatment targets for clinical implication in the future.